Metagenomic analysis of secondary cooling water microbial communities
Ruben Props
19 October, 2017
A. 16S analysis
Network analysis on relative abundances
In the first analysis we utilize the raw counts. As such this network will be built using relative abundance data.
# Set seed
set.seed(777)
# Import data
otus <- as.matrix(read.csv2("16S_data/OTU_table_raw.csv", fill = TRUE, header = TRUE,
row.names = 1))
taxa <- tax_table(as.matrix(read.csv2("16S_data/tax_table_raw.csv", fill = TRUE, header = TRUE,
row.names = 1))
)
phy_df <- phyloseq(otu_table(otus, taxa_are_rows = FALSE), taxa)
# filterobj <- filterTaxonMatrix(otus, minocc = 20,
# keepSum = TRUE, return.filtered.indices = TRUE)
# otus.f <- filterobj$mat
# taxa.f <- taxa[setdiff(1:nrow(taxa), filterobj$filtered.indices),]
# dummyTaxonomy <- colnames(tax_df); dummyTaxonomy[1] <- "Kingdom_dummy"
# taxa.f <- rbind(taxa.f, dummyTaxonomy)
# rownames(taxa.f)[nrow(taxa.f)] <- "0"
# rownames(otus.f)[nrow(otus.f)] <- "0"
#
# # Next, we assemble a new phyloseq object with the filtered OTU and taxonomy tables.
# updatedotus <- otu_table(otus.f, taxa_are_rows = TRUE)
# updatedtaxa <- tax_table(taxa.f)
# phyloseqobj.f <- phyloseq(updatedotus, updatedtaxa)
# Prevalence filtering
phy_df_filtered <- filter_taxa(phy_df, function(x) sum(x > 30) > (0.25*length(x)), TRUE)
sp_easi <- spiec.easi(phy_df_filtered, method='mb', lambda.min.ratio=1e-2,
nlambda=20, icov.select.params=list(rep.num=50))## Normalizing/clr transformation of data with pseudocount ...## Inverse Covariance Estimation with mb ...## Model selection with stars ...## Done!ig.mb <- adj2igraph(sp_easi$refit, vertex.attr = list(name=taxa_names(phy_df_filtered)))
vsize <- Biobase::rowMax(clr(otu_table(phy_df_filtered), 1))+15
Lineage_rel <- tax_table(phy_df_filtered)[,"Lineage"]
Lineage_rel <- factor(Lineage_rel, levels = unique(Lineage_rel))
vweights <- summary(symBeta(getOptBeta(sp_easi), mode='maxabs'))
MAGs <- c(); MAGs[taxa_names(phy_df_filtered)=="Otu00001"] <- "Limnohabitans MAG"
MAGs[taxa_names(phy_df_filtered)=="Otu00002"] <- "Bacteroidetes MAG1"
MAGs[taxa_names(phy_df_filtered)=="Otu00003"] <- "Bacteroidetes MAG2"
MAGs[is.na(MAGs)] <-""
# png(file = "./Figures/Figures_network/NETWORK-REL-CX-C30-A25.png", width = 9, height = 9, res = 500, units = "in")
plot_network_custom(ig.mb, phy_df_filtered, type='taxa',
line_weight = 2, hjust = 0.5,
point_size = 0.1, alpha = 0.01, label_size = 3.95)+
scale_fill_brewer(palette = "Paired")+
scale_color_brewer(palette = "Paired")+
geom_point(aes(size = vsize, fill = Lineage_rel), alpha = 0.5,
colour="black", shape=21)+
guides(size = FALSE,
fill = guide_legend(title = "Lineage", override.aes = list(size = 5),
nrow = 4),
color = FALSE)+
theme(legend.position="bottom", legend.text=element_text(size=12),
text = element_text(size = 12),
plot.margin = unit(c(1,1,1,1), "cm"))+
scale_size(range = c(5, 15))+
geom_label_repel(aes(label = MAGs), fontface = 'bold', color = 'black',
box.padding = 0.35, point.padding = 0.5,
segment.color = 'black',
size = 4,
# Width of the line segments.
segment.size = 1.5,
# Draw an arrow from the label to the data point.
arrow = arrow(length = unit(0.015, 'npc')),
nudge_x = -0.1,
nudge_y = 0.6
)
# dev.off()Network analysis on absolute abundances
The second network will be built using absolute abundance data by multiplying the relative taxon abundances by the total cell density. The final obtained counts will expressed as nr. of cells measured in 50 µL samples. We also only consider the OTUs that were left after the prevalence filtering conducted in the network construction with relative abundance data.
# Import cell count data
cell_counts <- read.csv("16S_data/cell_counts.csv")
cell_counts$sample_title <- as.factor(cell_counts$sample_title)
# Calculate proportions
phy_df_rel <- transform_sample_counts(phy_df, function(x) x/sum(x))
# Select samples for which corresponding counts are available
cell_counts <- cell_counts[cell_counts$sample_title %in% sample_names(phy_df_rel), ]
cell_counts <- droplevels(cell_counts)
phy_df_rel <- prune_samples(sample_names(phy_df_rel) %in% cell_counts$sample_title, phy_df_rel)
# Multiply with cell counts in 50 µL of sample
otu_table(phy_df_rel) <- otu_table(phy_df_rel) * cell_counts$Number_of_cells
# Select taxa that were selected based on prevalence in previous chunk
phy_df_abs <- prune_taxa(taxa_names(phy_df_filtered), phy_df_rel)
# Round absolute abundances to integers
otu_table(phy_df_abs) <- round(otu_table(phy_df_abs), 0)
# Construct network
sp_easi_abs <- spiec.easi(phy_df_abs, method='mb', lambda.min.ratio=1e-2,
nlambda=20, icov.select.params=list(rep.num=50))## Normalizing/clr transformation of data with pseudocount ...## Inverse Covariance Estimation with mb ...## Model selection with stars ...## Done!ig.mb_abs <- adj2igraph(sp_easi_abs$refit, vertex.attr = list(name=taxa_names(phy_df_abs)))
vsize_abs <- Biobase::rowMax(clr(otu_table(phy_df_abs), 1))+15
Lineage_abs <- tax_table(phy_df_abs)[,"Lineage"]
Lineage_abs <- factor(Lineage_abs, levels = unique(Lineage_abs))
vweights_abs <- summary(symBeta(getOptBeta(sp_easi_abs), mode='maxabs'))
MAGs <- c(); MAGs[taxa_names(phy_df_abs)=="Otu00001"] <- "Limnohabitans MAG"
MAGs[taxa_names(phy_df_abs)=="Otu00002"] <- "Bacteroidetes MAG1"
MAGs[taxa_names(phy_df_abs)=="Otu00003"] <- "Bacteroidetes MAG2"
MAGs[is.na(MAGs)] <-""
# Plot network inferred from absolute abundances
# png(file = "./Figures/Figures_network/NETWORK-ABS-CX-C30-A25.png", width = 9, height = 9, res = 500, units = "in")
plot_network_custom(ig.mb_abs, phy_df_abs, type='taxa',
line_weight = 2, hjust = 0.5,
point_size = 0.1, alpha = 0.01, label_size = 3.95)+
scale_fill_brewer(palette = "Paired")+
scale_color_brewer(palette = "Paired")+
geom_point(aes(size = vsize_abs, fill = Lineage_abs), alpha = 0.5,
colour="black", shape=21)+
guides(size = FALSE,
fill = guide_legend(title = "Lineage", override.aes = list(size = 5),
nrow = 4),
color = FALSE)+
theme(legend.position="bottom", legend.text=element_text(size=12),
text = element_text(size = 12),
plot.margin = unit(c(1,1,1,1), "cm"))+
scale_size(range = c(5, 15))+
geom_label_repel(aes(label = MAGs), fontface = 'bold', color = 'black',
box.padding = 0.35, point.padding = 0.5,
segment.color = 'black',
size = 4,
# Width of the line segments.
segment.size = 1.5,
# Draw an arrow from the label to the data point.
arrow = arrow(length = unit(0.015, 'npc')),
nudge_x = -0.1,
nudge_y = 0.6
)
# dev.off()B. MetaG analysis
# Read data
mean_coverage <- read.table("./SAMPLES-SUMMARY/bins_across_samples/mean_coverage.txt", header = TRUE)
std_coverage <- read.table("./SAMPLES-SUMMARY/bins_across_samples/std_coverage.txt", header = TRUE)
bin_size <- read.table("./SAMPLES-SUMMARY/general_bins_summary.txt", header = TRUE)[, c(1, 3, 6, 9)]
total_reads <- read.table("./sample_reads.tsv", header = TRUE)
read_length <- 300
# From wide to long format
mean_coverage_long <- gather(mean_coverage, Sample_ID, coverage,
SAMPLE_16:SAMPLE_65, factor_key=TRUE)
std_coverage_long <- gather(std_coverage, Sample_ID, std_coverage,
SAMPLE_16:SAMPLE_65,
factor_key=TRUE)
coverage_data <- data.frame(mean_coverage_long,
std_coverage = std_coverage_long[,3])
# Read and add metadata
# meta <- read.csv2("metadata.csv")
# meta$Sample_ID <- gsub(meta$Sample_ID, pattern = ".", replacement = "_", fixed = TRUE)
data_total <- left_join(coverage_data, total_reads, by = "Sample_ID")
data_total <- left_join(data_total, bin_size, by = "bins")
# data_total <- left_join(data_total, meta, by = "Sample_ID")
data_total$bins <- plyr::revalue(data_total$bins, c("BetIa_bin"="Limnohabitans_bin", "bacIa_vizbin1"="Bacteroidetes_bin1",
"bacIa_vizbin2"="Bacteroidetes_bin2"))
# Calculate relative abundance of the bins
data_total$mean_rel_abundance <- 100*(data_total$coverage*data_total$bin_size)/(read_length*data_total$Total_reads)
data_total$upper_rel_abundance <- 100*((data_total$coverage+data_total$std_coverage)*data_total$bin_size)/(read_length*data_total$Total_reads)
data_total$lower_rel_abundance <- 100*((data_total$coverage-data_total$std_coverage)*data_total$bin_size)/(read_length*data_total$Total_reads)
data_total$mean_rel_abundance_map <- 100*(data_total$coverage*data_total$bin_size)/(read_length*data_total$Mapped_reads)
data_total$upper_rel_abundance_map <- 100*((data_total$coverage+data_total$std_coverage)*data_total$bin_size)/(read_length*data_total$Mapped_reads)
data_total$lower_rel_abundance_map <- 100*((data_total$coverage-data_total$std_coverage)*data_total$bin_size)/(read_length*data_total$Mapped_reads)
# Add additional column that assigns PNCs to correct MAG
data_total$Genome_id <- factor(rep(c("Limnohabitans", "Limnohabitans", "Limnohabitans", "Bacteroidetes MAG2", "Bacteroidetes MAG1", "Bacteroidetes MAG2"), 4))
# Plot genome size for all three genomes
data_total[data_total$bins %in% c("Bacteroidetes_bin1",
"Bacteroidetes_bin2","Limnohabitans_bin"), ] %>%
ggplot(aes(x = bins, y = bin_size/(4*1e6), fill = Genome_id))+
theme_bw()+
geom_bar(width = 0.5, stat="identity", alpha = 0.7)+
coord_flip()+
scale_fill_manual(values = c(col_bac1, col_bac2, col_limno))+
theme(axis.text.x = element_text(angle = 0, size = 16),
axis.text.y = element_text(size = 16),
axis.title.x = element_text(size = 18),
legend.title = element_text("Genome bin"), legend.position = "top")+
xlab("")+
ylab("Genome size (Mbp)")+
guides(fill = FALSE)+
geom_hline(yintercept = 1, size = 2, linetype="dotted")
# Plot irep for all 3 genomes
data_total[data_total$bins %in% c("Bacteroidetes_bin1",
"Bacteroidetes_bin2","Limnohabitans_bin"), ] %>%
ggplot(aes(x = bins, y = mean_irep/4, fill = Genome_id))+
theme_bw()+
geom_bar(width = 0.5, stat="identity", alpha = 0.7)+
coord_flip()+
scale_fill_manual(values = c(col_bac1, col_bac2, col_limno))+
theme(axis.text.x = element_text(angle = 0, size = 16),
axis.text.y = element_text(size = 16),
axis.title.x = element_text(size = 18),
legend.title = element_text(""), legend.position = "top")+
xlab("")+
ylab("Index of Replication (iRep)")+
guides(fill = FALSE)+
geom_hline(yintercept = 1.34, size = 2, linetype="dotted")
1. Phylogenetic tree
Limnohabitans
RAxML tree for Limnohabitans MAG
Bacteroidetes
2. Investigate MAG- and 16S-based abundances
It is clear that there is significant %GC coverage bias present. The estimated relative abundances from metagenomics do not quantitatively match with the V3-V4 16S rRNA gene amplicon data. \[Relative\ abundance =100*(\frac{mean\ coverage * bin\ size}{read\ length*total\ sample\ reads })\] Another option is to calculate relative to mapped number of reads: \[Relative\ abundance =100*(\frac{mean\ coverage * bin\ size}{read\ length*total\ sample\ reads * \%mapped\ reads})\]
Import reference relative abundances from 16S data set in order to directly compare with metagenomic data set.
df_16S <- read.table("relative_abundance_16S.tsv", header = TRUE)
df_16S_long <- gather(df_16S, Sample_ID, relative_abundance_16S,
SAMPLE_16:SAMPLE_65, factor_key=TRUE)# Subset for only the three complete genomes (not PNCs).
data_total_sb <- data_total[data_total$bins %in% c("Limnohabitans_bin", "Bacteroidetes_bin1", "Bacteroidetes_bin2"),]
p_meta <- ggplot(data = data_total_sb, aes(x = bins, y = mean_rel_abundance, fill = bins))+
geom_point(size = 4, shape = 21, alpha = 0.7)+
scale_fill_manual(values = c(col_bac1, col_bac2, col_limno))+
theme_bw()+
geom_errorbar(aes(ymin=lower_rel_abundance,
ymax=upper_rel_abundance,
width=.1))+
facet_grid(.~Sample_ID)+
# ylim(0,1)+
theme(axis.text=element_text(size=14), axis.title=element_text(size=20),
title=element_text(size=20), legend.text=element_text(size=14),
legend.background = element_rect(fill="transparent"),
axis.text.x = element_text(angle = 45, hjust = 1),
strip.text.x=element_text(size=18))+
ylab("Mean relative abundance (%)")+
ylim(-1,100)+
ggtitle("Metagenomic - total reads")
# Corrected for mapped N° of reads
p_meta_mapped <- ggplot(data = data_total_sb, aes(x = bins, y = mean_rel_abundance_map, fill = bins))+
geom_point(size = 4, shape = 21, alpha = 0.7)+
scale_fill_manual(values = c(col_bac1, col_bac2, col_limno))+
theme_bw()+
geom_errorbar(aes(ymin=lower_rel_abundance_map,
ymax=upper_rel_abundance_map,
width=.1))+
facet_grid(.~Sample_ID)+
# ylim(0,1)+
theme(axis.text=element_text(size=14), axis.title=element_text(size=20),
title=element_text(size=20), legend.text=element_text(size=14),
legend.background = element_rect(fill="transparent"),
axis.text.x = element_text(angle = 45, hjust = 1),
strip.text.x=element_text(size=18))+
ylab("Mean relative abundance (%)")+
ylim(-1,100)+
ggtitle("Metagenomic - mapped reads")
p_16S <- ggplot(data = df_16S_long, aes(x = bins, y = relative_abundance_16S, fill = bins))+
geom_point(size = 4, shape = 21, alpha = 0.7)+
scale_fill_manual(values = c(col_bac1, col_bac2, col_limno))+
theme_bw()+
facet_grid(.~Sample_ID)+
theme(axis.text=element_text(size=14), axis.title=element_text(size=20),
title=element_text(size=20), legend.text=element_text(size=14),
legend.background = element_rect(fill="transparent"),
axis.text.x = element_text(angle = 45, hjust = 1),
strip.text.x=element_text(size=18))+
ylab("Mean relative abundance (%)")+
ylim(-1,100)+
ggtitle("V3-V4 16S")
grid_arrange_shared_legend(p_meta, p_meta_mapped, p_16S, ncol = 1, nrow = 3)
3. Investigate sequence characteristics within coding DNA sequences (CDS)
# First we need the files that map the gene ID to the sequence ID (linux cmd: https://github.com/rprops/MetaG_lakeMI/wiki/11.-Genome-annotation)
# These are stored in the IMG_annotation data for each genome bin
# Next, extract the %GC of each gene from the gff file
extract_gc_from_gff("./IMG_annotation/IMG_2724679690_Limnohabitans/121950.assembled.gff", outputFolder = "GC_analysis")
extract_gc_from_gff("./IMG_annotation/IMG_2724679691_Bacteroidetes_bin1/121951.assembled.gff", outputFolder = "GC_analysis")
extract_gc_from_gff("./IMG_annotation/IMG_2724679698_Bacteroidetes_bin2/121960.assembled.gff", outputFolder = "GC_analysis")
# Use these files to make dataframes mapping function (COGs/Pfams/KO) and %GC
LIMNO_gc_cog <- gc2function(seq_id_gc = "GC_analysis/seqid_GC_121950.assembled.gff.tsv", gene_id_seq_id ="./IMG_annotation/IMG_2724679690_Limnohabitans/Annotation/2724679690_gene_oid_2_seq_id.txt",
functions = "./IMG_annotation/IMG_2724679690_Limnohabitans/Annotation/2724679690.cog.tab.txt", gc_thresh = 0.1, output = FALSE)## Thu Oct 19 17:20:14 2017 --- There are 2830 genes with > 0.1 %
## Thu Oct 19 17:20:14 2017 --- This is 100 % of all genes
## Thu Oct 19 17:20:14 2017 --- The 10 genes with the highest GC% are:
## function_id function_name
## 2821 COG0405 Gamma-glutamyltranspeptidase
## 2822 COG2755 Lysophospholipase L1 or related esterase
## 2823 COG0642 Signal transduction histidine kinase
## 2824 COG1514 2'-5' RNA ligase
## 2825 COG1767 Triphosphoribosyl-dephospho-CoA synthetase
## 2826 COG1261 Flagella basal body P-ring formation protein FlgA
## 2827 COG0665 Glycine/D-amino acid oxidase (deaminating)
## 2828 COG0810 Periplasmic protein TonB, links inner and outer membranes
## 2829 COG1040 Predicted amidophosphoribosyltransferases
## 2830 COG1240 Mg-chelatase subunit ChlD
## GC
## 2821 78.5
## 2822 78.5
## 2823 78.5
## 2824 78.5
## 2825 78.6
## 2826 78.6
## 2827 79.1
## 2828 79.5
## 2829 79.7
## 2830 80.6BAC1_gc_cog <- gc2function(seq_id_gc = "GC_analysis/seqid_GC_121951.assembled.gff.tsv", gene_id_seq_id ="./IMG_annotation/IMG_2724679691_Bacteroidetes_bin1/Annotation/2724679691_gene_oid_2_seq_id.txt",
functions = "./IMG_annotation/IMG_2724679691_Bacteroidetes_bin1//Annotation/2724679691.cog.tab.txt", gc_thresh = 0.1, output = FALSE)## Thu Oct 19 17:20:14 2017 --- There are 1889 genes with > 0.1 %
## Thu Oct 19 17:20:14 2017 --- This is 100 % of all genes
## Thu Oct 19 17:20:14 2017 --- The 10 genes with the highest GC% are:
## function_id
## 1880 COG0052
## 1881 COG0183
## 1882 COG0629
## 1883 COG0509
## 1884 COG1734
## 1885 COG0636
## 1886 COG3502
## 1887 COG1765
## 1888 COG0377
## 1889 COG4104
## function_name
## 1880 Ribosomal protein S2
## 1881 Acetyl-CoA acetyltransferase
## 1882 Single-stranded DNA-binding protein
## 1883 Glycine cleavage system H protein (lipoate-binding)
## 1884 RNA polymerase-binding transcription factor DksA
## 1885 FoF1-type ATP synthase, membrane subunit c/Archaeal/vacuolar-type H+-ATPase, subunit K
## 1886 Uncharacterized conserved protein, DUF952 family
## 1887 Uncharacterized OsmC-related protein
## 1888 NADH:ubiquinone oxidoreductase 20 kD subunit (chhain B) or related Fe-S oxidoreductase
## 1889 Zn-binding Pro-Ala-Ala-Arg (PAAR) domain, incolved in TypeVI secretion
## GC
## 1880 51.6
## 1881 51.6
## 1882 52.0
## 1883 52.6
## 1884 52.7
## 1885 52.8
## 1886 52.9
## 1887 53.6
## 1888 53.6
## 1889 59.3BAC2_gc_cog <- gc2function(seq_id_gc = "GC_analysis/seqid_GC_121960.assembled.gff.tsv", gene_id_seq_id ="./IMG_annotation/IMG_2724679698_Bacteroidetes_bin2/Annotation/2724679698_gene_oid_2_seq_id.txt",
functions = "./IMG_annotation/IMG_2724679698_Bacteroidetes_bin2/Annotation/2724679698.cog.tab.txt", gc_thresh = 0.1, output = FALSE)## Thu Oct 19 17:20:14 2017 --- There are 1797 genes with > 0.1 %
## Thu Oct 19 17:20:14 2017 --- This is 100 % of all genes
## Thu Oct 19 17:20:14 2017 --- The 10 genes with the highest GC% are:
## function_id
## 1788 COG4675
## 1789 COG0636
## 1790 COG1501
## 1791 COG0725
## 1792 COG5434
## 1793 COG2115
## 1794 COG4225
## 1795 COG3669
## 1796 COG4588
## 1797 COG3258
## function_name
## 1788 Microcystin-dependent protein (function unknown)
## 1789 FoF1-type ATP synthase, membrane subunit c/Archaeal/vacuolar-type H+-ATPase, subunit K
## 1790 Alpha-glucosidase, glycosyl hydrolase family GH31
## 1791 ABC-type molybdate transport system, periplasmic component
## 1792 Polygalacturonase
## 1793 Xylose isomerase
## 1794 Rhamnogalacturonyl hydrolase YesR
## 1795 Alpha-L-fucosidase
## 1796 Accessory colonization factor AcfC, contains ABC-type periplasmic domain
## 1797 Cytochrome c
## GC
## 1788 44.5
## 1789 44.6
## 1790 44.9
## 1791 45.0
## 1792 45.1
## 1793 45.1
## 1794 45.4
## 1795 45.5
## 1796 46.6
## 1797 46.8LIMNO_gc_pfam <- gc2function(seq_id_gc = "GC_analysis/seqid_GC_121950.assembled.gff.tsv", gene_id_seq_id ="./IMG_annotation/IMG_2724679690_Limnohabitans/Annotation/2724679690_gene_oid_2_seq_id.txt",
functions = "./IMG_annotation/IMG_2724679690_Limnohabitans/Annotation/2724679690.pfam.tab.txt", gc_thresh = 0.1, output = FALSE)## Thu Oct 19 17:20:15 2017 --- There are 4954 genes with > 0.1 %
## Thu Oct 19 17:20:15 2017 --- This is 100 % of all genes
## Thu Oct 19 17:20:15 2017 --- The 10 genes with the highest GC% are:
## function_id function_name GC
## 4945 pfam13202 EF-hand_5 79.0
## 4946 pfam16537 T2SSB 79.0
## 4947 pfam01266 DAO 79.1
## 4948 pfam03544 TonB_C 79.5
## 4949 pfam00156 Pribosyltran 79.7
## 4950 pfam11142 DUF2917 79.8
## 4951 pfam13318 DUF4089 79.8
## 4952 pfam13519 VWA_2 80.6
## 4953 pfam02120 Flg_hook 80.9
## 4954 pfam03023 MVIN 81.7BAC1_gc_pfam <- gc2function(seq_id_gc = "GC_analysis/seqid_GC_121951.assembled.gff.tsv", gene_id_seq_id ="./IMG_annotation/IMG_2724679691_Bacteroidetes_bin1/Annotation/2724679691_gene_oid_2_seq_id.txt",
functions = "./IMG_annotation/IMG_2724679691_Bacteroidetes_bin1//Annotation/2724679691.pfam.tab.txt", gc_thresh = 0.1, output = FALSE)## Thu Oct 19 17:20:15 2017 --- There are 3929 genes with > 0.1 %
## Thu Oct 19 17:20:15 2017 --- This is 100 % of all genes
## Thu Oct 19 17:20:15 2017 --- The 10 genes with the highest GC% are:
## function_id function_name GC
## 3920 pfam02803 Thiolase_C 51.6
## 3921 pfam00436 SSB 52.0
## 3922 pfam00171 Aldedh 52.2
## 3923 pfam01597 GCV_H 52.6
## 3924 pfam01258 zf-dskA_traR 52.7
## 3925 pfam00137 ATP-synt_C 52.8
## 3926 pfam06108 DUF952 52.9
## 3927 pfam02566 OsmC 53.6
## 3928 pfam01058 Oxidored_q6 53.6
## 3929 pfam05488 PAAR_motif 59.3BAC2_gc_pfam <- gc2function(seq_id_gc = "GC_analysis/seqid_GC_121960.assembled.gff.tsv", gene_id_seq_id ="./IMG_annotation/IMG_2724679698_Bacteroidetes_bin2/Annotation/2724679698_gene_oid_2_seq_id.txt",
functions = "./IMG_annotation/IMG_2724679698_Bacteroidetes_bin2/Annotation/2724679698.pfam.tab.txt", gc_thresh = 0.1, output = FALSE)## Thu Oct 19 17:20:15 2017 --- There are 3573 genes with > 0.1 %
## Thu Oct 19 17:20:15 2017 --- This is 100 % of all genes
## Thu Oct 19 17:20:15 2017 --- The 10 genes with the highest GC% are:
## function_id function_name GC
## 3564 pfam13531 SBP_bac_11 46.6
## 3565 pfam13442 Cytochrome_CBB3 46.8
## 3566 pfam12779 YXWGXW 50.0
## 3567 pfam12779 YXWGXW 50.0
## 3568 pfam01391 Collagen 50.4
## 3569 pfam01391 Collagen 50.4
## 3570 pfam01391 Collagen 50.4
## 3571 pfam01391 Collagen 50.4
## 3572 pfam01391 Collagen 50.4
## 3573 pfam01391 Collagen 50.4LIMNO_gc_KO <- gc2function(seq_id_gc = "GC_analysis/seqid_GC_121950.assembled.gff.tsv", gene_id_seq_id ="./IMG_annotation/IMG_2724679690_Limnohabitans/Annotation/2724679690_gene_oid_2_seq_id.txt",
functions = "./IMG_annotation/IMG_2724679690_Limnohabitans/Annotation/2724679690.ko.tab.txt", gc_thresh = 0.1, output = FALSE)## Thu Oct 19 17:20:15 2017 --- There are 2164 genes with > 0.1 %
## Thu Oct 19 17:20:15 2017 --- This is 100 % of all genes
## Thu Oct 19 17:20:15 2017 --- The 10 genes with the highest GC% are:
## function_id
## 2155 two-component system, OmpR family, sensor histidine kinase QseC [EC:2.7.13.3]
## 2156 2'-5' RNA ligase [EC:6.5.1.-]
## 2157 triphosphoribosyl-dephospho-CoA synthase [EC:2.4.2.52]
## 2158 flagella basal body P-ring formation protein FlgA
## 2159 general secretion pathway protein B
## 2160 tRNA 5-methylaminomethyl-2-thiouridine biosynthesis bifunctional protein [EC:2.1.1.61 1.5.-.-]
## 2161 tRNA 5-methylaminomethyl-2-thiouridine biosynthesis bifunctional protein [EC:2.1.1.61 1.5.-.-]
## 2162 4'-phosphopantetheinyl transferase [EC:2.7.8.-]
## 2163 magnesium chelatase subunit D [EC:6.6.1.1]
## 2164 flagellar hook-length control protein FliK
## function_name GC
## 2155 EC:2.7.13.3 78.5
## 2156 EC:6.5.1.- 78.5
## 2157 EC:2.4.2.52 78.6
## 2158 78.6
## 2159 79.0
## 2160 EC:1.5.- 79.1
## 2161 EC:2.1.1.61 79.1
## 2162 EC:2.7.8.- 79.1
## 2163 EC:6.6.1.1 80.6
## 2164 80.9BAC1_gc_KO <- gc2function(seq_id_gc = "GC_analysis/seqid_GC_121951.assembled.gff.tsv", gene_id_seq_id ="./IMG_annotation/IMG_2724679691_Bacteroidetes_bin1/Annotation/2724679691_gene_oid_2_seq_id.txt",
functions = "./IMG_annotation/IMG_2724679691_Bacteroidetes_bin1//Annotation/2724679691.ko.tab.txt", gc_thresh = 0.1, output = FALSE)## Thu Oct 19 17:20:15 2017 --- There are 1384 genes with > 0.1 %
## Thu Oct 19 17:20:15 2017 --- This is 100 % of all genes
## Thu Oct 19 17:20:15 2017 --- The 10 genes with the highest GC% are:
## function_id function_name GC
## 1375 single-strand DNA-binding protein 51.3
## 1376 threonine aldolase [EC:4.1.2.5] EC:4.1.2.5 51.3
## 1377 large subunit ribosomal protein L32 51.3
## 1378 uncharacterized protein 51.5
## 1379 translation initiation factor IF-2 51.5
## 1380 small subunit ribosomal protein S2 51.6
## 1381 single-strand DNA-binding protein 52.0
## 1382 glycine cleavage system H protein 52.6
## 1383 F-type H+-transporting ATPase subunit c 52.8
## 1384 NADH-quinone oxidoreductase subunit B [EC:1.6.5.3] EC:1.6.5.3 53.6BAC2_gc_KO <- gc2function(seq_id_gc = "GC_analysis/seqid_GC_121960.assembled.gff.tsv", gene_id_seq_id ="./IMG_annotation/IMG_2724679698_Bacteroidetes_bin2/Annotation/2724679698_gene_oid_2_seq_id.txt",
functions = "./IMG_annotation/IMG_2724679698_Bacteroidetes_bin2/Annotation/2724679698.ko.tab.txt", gc_thresh = 0.1, output = FALSE)## Thu Oct 19 17:20:15 2017 --- There are 1342 genes with > 0.1 %
## Thu Oct 19 17:20:15 2017 --- This is 100 % of all genes
## Thu Oct 19 17:20:15 2017 --- The 10 genes with the highest GC% are:
## function_id function_name GC
## 1333 uncharacterized protein 43.9
## 1334 NADH-quinone oxidoreductase subunit B [EC:1.6.5.3] EC:1.6.5.3 44.3
## 1335 F-type H+-transporting ATPase subunit c 44.6
## 1336 alpha-D-xyloside xylohydrolase [EC:3.2.1.177] EC:3.2.1.177 44.9
## 1337 xylose isomerase [EC:5.3.1.5] EC:5.3.1.5 45.1
## 1338 alpha-L-fucosidase [EC:3.2.1.51] EC:3.2.1.51 45.4
## 1339 uncharacterized protein 45.5
## 1340 uncharacterized protein 45.8
## 1341 accessory colonization factor AcfC 46.6
## 1342 thiosulfate dehydrogenase [EC:1.8.2.2] EC:1.8.2.2 46.8Motivation: For COGs there exists a hierarchy allowing us to investigate whether there is a conservation of high/low %GC in certain functional gene groups. In order to do this we need to incorporate this hierarchy into the genome dataframes we have now.
4. Analysis of gene length distribution
Here we use the dataframe made in the previous section to see if there is a significant difference in the gene length of the COGs within these three consensus genomes.
Observation: They have very small genes: on average < 500bp.
# Limnohabitans MAG gene length distribution
p_LIMNO_length <- easyGgplot2::ggplot2.histogram(data = LIMNO_gc_cog, xName = 'gene_length',
groupName = 'Genome', alpha = 0.5,
legendPosition = "top", binwidth = 0.15,
groupColors = col_limno,addMeanLine=TRUE, meanLineColor="black",
meanLineType="dashed")+ theme_bw()+ ylim(0,15)+
labs(x = "Gene length (bp)", y = "Count")+ theme(legend.position="none")+
ggtitle("Limnohabitans MAG")+ xlim(0,2000)
# Bacteroidetes MAG1 gene length distribution
p_BAC1_length <- easyGgplot2::ggplot2.histogram(data = BAC1_gc_cog, xName = 'gene_length',
groupName = 'Genome', alpha = 0.5,
legendPosition = "top", binwidth = 0.15,
groupColors = col_bac1,addMeanLine=TRUE, meanLineColor="black",
meanLineType="dashed")+ theme_bw()+ ylim(0,15)+
labs(x = "Gene length (bp)", y = "Count")+ theme(legend.position="none")+
ggtitle("Bacteroidetes MAG1")+ xlim(0,2000)
# Bacteroidetes MAG2 gene length distribution
p_BAC2_length <- easyGgplot2::ggplot2.histogram(data = BAC2_gc_cog, xName = 'gene_length',
groupName = 'Genome', alpha = 0.5,
legendPosition = "top", binwidth = 0.15,
groupColors = col_bac2,addMeanLine=TRUE, meanLineColor="black",
meanLineType="dashed")+ theme_bw()+ ylim(0,15)+
labs(x = "Gene length (bp)", y = "Count")+ theme(legend.position="none")+
ggtitle("Bacteroidetes MAG2")+ xlim(0,2000)
grid.arrange(p_LIMNO_length, p_BAC1_length, p_BAC2_length, ncol = 3)
We can do the same for the Pfams.
# Limnohabitans MAG gene length distribution
p_LIMNO_length <- easyGgplot2::ggplot2.histogram(data = LIMNO_gc_pfam, xName = 'gene_length',
groupName = 'Genome', alpha = 0.5,
legendPosition = "top", binwidth = 0.15,
groupColors = col_limno,addMeanLine=TRUE, meanLineColor="black",
meanLineType="dashed")+ theme_bw()+ ylim(0,25)+
labs(x = "Gene length (bp)", y = "Count")+ theme(legend.position="none")+
ggtitle("Limnohabitans MAG")+ xlim(0,3000)
# Bacteroidetes MAG1 gene length distribution
p_BAC1_length <- easyGgplot2::ggplot2.histogram(data = BAC1_gc_pfam, xName = 'gene_length',
groupName = 'Genome', alpha = 0.5,
legendPosition = "top", binwidth = 0.15,
groupColors = col_bac1,addMeanLine=TRUE, meanLineColor="black",
meanLineType="dashed")+ theme_bw()+ ylim(0,25)+
labs(x = "Gene length (bp)", y = "Count")+ theme(legend.position="none")+
ggtitle("Bacteroidetes MAG1")+ xlim(0,3000)
# Bacteroidetes MAG2 gene length distribution
p_BAC2_length <- easyGgplot2::ggplot2.histogram(data = BAC2_gc_pfam, xName = 'gene_length',
groupName = 'Genome', alpha = 0.5,
legendPosition = "top", binwidth = 0.15,
groupColors = col_bac2,addMeanLine=TRUE, meanLineColor="black",
meanLineType="dashed")+ theme_bw()+ ylim(0,25)+
labs(x = "Gene length (bp)", y = "Count")+ theme(legend.position="none")+
ggtitle("Bacteroidetes MAG2")+ xlim(0,3000)
grid.arrange(p_LIMNO_length, p_BAC1_length, p_BAC2_length, ncol = 3)
5. Identify unique functional genes (COG/Pfams)
# Find unique functions in Limnohabitans MAG vs Bacteroidetes MAG1
unique_LIMNO_BAC1 <- dplyr::anti_join(LIMNO_gc_cog, BAC1_gc_cog, by = "cog_id")
cat("There are", paste(nrow(unique_LIMNO_BAC1)), "unique COGs in Limnohabitans MAG vs Bacteroidetes MAG1")## There are 1178 unique COGs in Limnohabitans MAG vs Bacteroidetes MAG1# Find unique functions in Limnohabitans MAG vs Bacteroidetes MAG2
unique_LIMNO_BAC2 <- dplyr::anti_join(LIMNO_gc_cog, BAC2_gc_cog, by = "cog_id")
cat("There are", paste(nrow(unique_LIMNO_BAC2)), "unique COGs in Limnohabitans MAG vs Bacteroidetes MAG2")## There are 1143 unique COGs in Limnohabitans MAG vs Bacteroidetes MAG2# Find unique functions in Bacteroidetes MAG1 vs Bacteroidetes MAG2
unique_BAC1_BAC2 <- dplyr::anti_join(BAC1_gc_cog, BAC2_gc_cog, by = "cog_id")
cat("There are", paste(nrow(unique_BAC1_BAC2)), "unique COGs in Bacteroidetes MAG1 vs Bacteroidetes MAG2")## There are 153 unique COGs in Bacteroidetes MAG1 vs Bacteroidetes MAG2# Find unique functions in Bacteroidetes MAG1 vs Bacteroidetes MAG2
unique_BAC2_BAC1 <- dplyr::anti_join(BAC2_gc_cog, BAC1_gc_cog, by = "cog_id")
cat("There are", paste(nrow(unique_BAC2_BAC1)), "unique COGs in Bacteroidetes MAG2 vs Bacteroidetes MAG1")## There are 143 unique COGs in Bacteroidetes MAG2 vs Bacteroidetes MAG1# Find unique functions in Limnohabitans MAG vs Bacteroidetes MAG1
unique_pfam_LIMNO_BAC1 <- dplyr::anti_join(LIMNO_gc_pfam, BAC1_gc_pfam, by = "pfam_id")
cat("There are", paste(nrow(unique_pfam_LIMNO_BAC1)), "unique Pfams in Limnohabitans MAG vs Bacteroidetes MAG1")## There are 1474 unique Pfams in Limnohabitans MAG vs Bacteroidetes MAG1# Find unique functions in Limnohabitans MAG vs Bacteroidetes MAG2
unique_pfam_LIMNO_BAC2 <- dplyr::anti_join(LIMNO_gc_pfam, BAC2_gc_pfam, by = "pfam_id")
cat("There are", paste(nrow(unique_pfam_LIMNO_BAC2)), "unique Pfams in Limnohabitans MAG vs Bacteroidetes MAG2")## There are 1424 unique Pfams in Limnohabitans MAG vs Bacteroidetes MAG2# Find unique functions in Bacteroidetes MAG1 vs Bacteroidetes MAG2
unique_pfam_BAC1_BAC2 <- dplyr::anti_join(BAC1_gc_pfam, BAC2_gc_pfam, by = "pfam_id")
cat("There are", paste(nrow(unique_pfam_BAC1_BAC2)), "unique Pfams in Bacteroidetes MAG1 vs Bacteroidetes MAG2")## There are 285 unique Pfams in Bacteroidetes MAG1 vs Bacteroidetes MAG2# Find unique functions in Bacteroidetes MAG1 vs Bacteroidetes MAG2
unique_pfam_BAC2_BAC1 <- dplyr::anti_join(BAC2_gc_pfam, BAC1_gc_pfam, by = "pfam_id")
cat("There are", paste(nrow(unique_pfam_BAC2_BAC1)), "unique Pfams in Bacteroidetes MAG2 vs Bacteroidetes MAG1")## There are 233 unique Pfams in Bacteroidetes MAG2 vs Bacteroidetes MAG16. COG functional categories
Get COG ID to COG functional category mapping file here: ftp://ftp.ncbi.nih.gov/pub/wolf/COGs/COG0303/cogs.csv
The exact statistical analysis to compare genomes based on these profiles should be performed in STAMP.
# Import COG mapping file
cogid_2_cogcat <- read.csv("./Mapping_files/cogid_2_cogcat.csv", sep = ",", header = FALSE, fill = TRUE,col.names = c("COG_ID", "COG_class", "function"))[, 1:2]
cogid_2_cogcat <- cogid_2_cogcat[(cogid_2_cogcat$COG_class)!="", ]
cogid_2_cogcat <- droplevels(cogid_2_cogcat)
# Read COG category file
cog_categories <- read.table("./Mapping_files/cog_categories.tsv", header = TRUE, sep = "\t")
# Merge COG metadata
cog_meta <- dplyr::left_join(cog_categories, cogid_2_cogcat, by = c("COG_class" = "COG_class"))
cog_meta <- droplevels(cog_meta)
# Merge genome information of all genome bins
merged_gc_cog <- rbind(LIMNO_gc_cog, BAC1_gc_cog, BAC2_gc_cog)
merged_gc_cog <- data.frame(merged_gc_cog, genome_id = c(rep("Limnohabitans MAG", nrow(LIMNO_gc_cog)),
rep("Bacteroidetes MAG1", nrow(BAC1_gc_cog)),
rep("Bacteroidetes MAG2", nrow(BAC2_gc_cog)))
)
# Merge this metadata with the genome data from before
# COGs with multiple classifications are currently still NA - work on this.
merged_gc_cog <- dplyr::left_join(merged_gc_cog, cog_meta, by = c("cog_id" = "COG_ID"))
merged_gc_cog <- merged_gc_cog[!is.na(merged_gc_cog$COG_functional_category),]
# Visualize distribution across major metabolism functional COG groups per genome.
p_cog_func_group <- ggplot(data = merged_gc_cog,
aes(x=COG_functional_category, fill = COG_functional_cluster))+
geom_bar(stat="count", width=0.7, color = "black", size = 0.75)+
theme_bw()+
facet_grid(genome_id~.)+
scale_fill_brewer(palette = "Accent")+
labs(x = "Gene length (bp)", y = "Count")+
theme(legend.position="bottom", axis.text.x = element_text(angle = 90, hjust = 1),
legend.text = element_text(size = 5))+
guides(fill=guide_legend(nrow=2,byrow=TRUE))
print(p_cog_func_group)
p_cog_func_clust <- ggplot(data = merged_gc_cog,
aes(x=COG_functional_cluster, fill = COG_functional_cluster))+
geom_bar(stat="count", width=0.7, color = "black", size = 0.75)+
theme_bw()+
facet_grid(genome_id~.)+
scale_fill_brewer(palette = "Accent")+
labs(x = "Gene length (bp)", y = "Count")+
theme(legend.position="bottom",axis.text.x = element_text(angle = 90, hjust = 1),
legend.text = element_text(size = 5))+
guides(fill=guide_legend(nrow=2,byrow=TRUE))
print(p_cog_func_clust)
7. KO pathways
- Get reference file that maps KO ids to pathways here: http://www.genome.jp/kegg-bin/get_htext?ko00001.keg (download htext).
# Import data
ko_path_df <- read.table("./Mapping_files/ko00001.keg", header = FALSE, sep = ";",
skip = 8, quote = "", fill = TRUE,
col.names = c("Level", "KO", "Function_abbrev", "Function_spec"))
ko_path_df <- ko_path_df[1:(nrow(ko_path_df)-1), ] # remove tailing "!" at the end of file
# Remove empty rows
ko_path_df <- ko_path_df[!ko_path_df$KO == "", ]
# Extract pathways from dataframe and add them as new factor column
# pathw_hier <- data.frame(Level = ko_path_df$Level[ko_path_df$Level %in% c("A","B","C")],
# Name = ko_path_df$KO[ko_path_df$Level %in% c("A","B","C")]
# )
# pathw_hier$Name <- as.character(pathw_hier$Name)
ko_path_df <- data.frame(ko_path_df, level_A = "A", level_B = "B", level_C = "C",
stringsAsFactors = FALSE)
ko_path_df$KO <- as.character(ko_path_df$KO)
# Get positions where to replicate higher hierarichcal level
pos_A <- c(c(1:nrow(ko_path_df))[ko_path_df$Level %in% "A"], nrow(ko_path_df)+1)
pos_B <- c(c(1:nrow(ko_path_df))[ko_path_df$Level %in% "B"], nrow(ko_path_df)+1)
pos_C <- c(c(1:nrow(ko_path_df))[ko_path_df$Level %in% "C"], nrow(ko_path_df)+1)
for(i in 1:(length(pos_A)-1)){
ko_path_df$level_A[pos_A[i]:(pos_A[i+1]-1)] <- ko_path_df$KO[pos_A[i]]
}
for(i in 1:(length(pos_B)-1)){
ko_path_df$level_B[pos_B[i]:(pos_B[i+1]-1)] <- ko_path_df$KO[pos_B[i]]
}
for(i in 1:(length(pos_C)-1)){
ko_path_df$level_C[pos_C[i]:(pos_C[i+1]-1)] <- ko_path_df$KO[pos_C[i]]
}
# Remove all rows with level A, B, C - and level column
ko_path_df <- ko_path_df[!ko_path_df$Level %in% c("A", "B", "C"), ]
ko_path_df$level_A <- gsub(ko_path_df$level_A, pattern = "<b>|</b>", replacement = "")
ko_path_df$level_B <- gsub(ko_path_df$level_B, pattern = "<b>|</b>", replacement = "")
ko_path_df <- ko_path_df[, -1]
# Annotate merged ko file
merged_gc_ko <- rbind(LIMNO_gc_KO, BAC1_gc_KO, BAC2_gc_KO)
merged_gc_ko <- data.frame(merged_gc_ko,
genome_id = c(rep("Limnohabitans MAG", nrow(LIMNO_gc_KO)),
rep("Bacteroidetes MAG1", nrow(BAC1_gc_KO)),
rep("Bacteroidetes MAG2", nrow(BAC2_gc_KO)))
)
merged_gc_ko$ko_id <- gsub(merged_gc_ko$ko_id, pattern = "KO:", replacement = "")
merged_gc_ko <- dplyr::left_join(merged_gc_ko, ko_path_df[, c(1, 4:6)], by = c("ko_id" = "KO"))
# Fill up NA slots with "Unknown" pathway
merged_gc_ko$level_A[is.na(merged_gc_ko$level_A)] <- "Unknown"
merged_gc_ko$level_B[is.na(merged_gc_ko$level_B)] <- "Unknown"
merged_gc_ko$level_C[is.na(merged_gc_ko$level_C)] <- "Unknown"8. Synonymous Codon Usage Bias analysis using CodonO
Get CodonO for linux here: http://sysbio.cvm.msstate.edu/software/CodonO/download
- Run
CU.linux input.genes.fna
Output from CodonO:
inputfile.ok —- the SCUO units for each input sequence based on its order
inputfile.fik —- the composition ratio of the i-th amino acid in the k-th sequence
inputfile.hijk —- the frequency of the j-th degenerate codon for amino acid i in each sequenceThis output was not sufficient to perform any analysis. Therefore I used the web browser to calculate the synonymous codon bias in the genes of the three genomes.
** Codon bias is proportional to mRNA production! (Wan et al 2004)
** However, strong correlation between SCUO and %GC has been reported, thereby confounding possible “biological” effects.. Beware..
# Import and format data for Limnohabitans MAG
SCUO_LIMNO <- read.table("./IMG_annotation/IMG_2724679690_Limnohabitans/Annotation/2724679690.genes.fna.codonO.output",
sep = ",", blank.lines.skip = TRUE, allowEscapes = FALSE, skipNul = TRUE
)
SCUO_LIMNO$V1 <- gsub(SCUO_LIMNO$V1, pattern = "\t", replacement = "")
Gene_LIMNO <- do.call(rbind, strsplit(SCUO_LIMNO$V1[grep("Ga0*", SCUO_LIMNO$V1)], " "))[,2]
SCUO_LIMNO$V1 <- gsub(SCUO_LIMNO$V1, pattern = " ", replacement = "")
SCUO_LIMNO <- data.frame(GC = SCUO_LIMNO$V1[grep(x = SCUO_LIMNO$V1, pattern = "GC*.*=")],
SCUO = rep(SCUO_LIMNO$V1[grep(x = SCUO_LIMNO$V1, pattern = "SCUO*")], each = 4),
Gene = rep(Gene_LIMNO, each = 4)
)
SCUO_LIMNO$GC <- as.numeric(gsub(SCUO_LIMNO$GC, pattern = ".*=", replacement = ""))
SCUO_LIMNO$SCUO <- as.numeric(gsub(SCUO_LIMNO$SCUO, pattern = ".*=", replacement = ""))
# Import and format data for Bacteroidetes MAG1
SCUO_BAC1 <- read.table("./IMG_annotation/IMG_2724679691_Bacteroidetes_bin1/Annotation/2724679691.genes.fna.codonO.output",
sep = ",", blank.lines.skip = TRUE, allowEscapes = FALSE, skipNul = TRUE)
SCUO_BAC1$V1 <- gsub(SCUO_BAC1$V1, pattern = "\t", replacement = "")
Gene_BAC1 <- do.call(rbind, strsplit(SCUO_BAC1$V1[grep("Ga0*", SCUO_BAC1$V1)], " "))[,2]
SCUO_BAC1$V1 <- gsub(SCUO_BAC1$V1, pattern = " ", replacement = "")
SCUO_BAC1 <- data.frame(GC = SCUO_BAC1$V1[grep(x = SCUO_BAC1$V1, pattern = "GC*.*=")],
SCUO = rep(SCUO_BAC1$V1[grep(x = SCUO_BAC1$V1, pattern = "SCUO*")], each = 4),
Gene = rep(Gene_BAC1, each = 4)
)
SCUO_BAC1$GC <- as.numeric(gsub(SCUO_BAC1$GC, pattern = ".*=", replacement = ""))
SCUO_BAC1$SCUO <- as.numeric(gsub(SCUO_BAC1$SCUO, pattern = ".*=", replacement = ""))
# Import and format data for Bacteroidetes MAG2
SCUO_BAC2 <- read.table("./IMG_annotation/IMG_2724679698_Bacteroidetes_bin2/Annotation/2724679698.genes.fna.codonO.output",
sep = ",", blank.lines.skip = TRUE, allowEscapes = FALSE, skipNul = TRUE)
SCUO_BAC2$V1 <- gsub(SCUO_BAC2$V1, pattern = "\t", replacement = "")
Gene_BAC2 <- do.call(rbind, strsplit(SCUO_BAC2$V1[grep("Ga0*", SCUO_BAC2$V1)], " "))[,2]
SCUO_BAC2$V1 <- gsub(SCUO_BAC2$V1, pattern = " ", replacement = "")
SCUO_BAC2 <- data.frame(GC = SCUO_BAC2$V1[grep(x = SCUO_BAC2$V1, pattern = "GC*.*=")],
SCUO = rep(SCUO_BAC2$V1[grep(x = SCUO_BAC2$V1, pattern = "SCUO*")], each = 4),
Gene = rep(Gene_BAC2, each = 4)
)
SCUO_BAC2$GC <- as.numeric(gsub(SCUO_BAC2$GC, pattern = ".*=", replacement = ""))
SCUO_BAC2$SCUO <- as.numeric(gsub(SCUO_BAC2$SCUO, pattern = ".*=", replacement = ""))
# Merge data to one dataframe
SCUO_merged_gen <- data.frame(rbind(SCUO_LIMNO, SCUO_BAC1, SCUO_BAC2),
Genome_ID = c(rep("Limnohabitans MAG", nrow(SCUO_LIMNO)), rep("Bacteroidetes MAG1", nrow(SCUO_BAC1)),
rep("Bacteroidetes MAG2", nrow(SCUO_BAC2))),
GCx = rep(c("GC_mean", "GC1", "GC2", "GC3"),
(nrow(SCUO_LIMNO) + nrow(SCUO_BAC1) + nrow(SCUO_BAC2))/4
)
)
# Merge codon bias data with KO pathway annotation
SCUO_merged <- dplyr::left_join(SCUO_merged_gen, merged_gc_ko[, c(1:2,4 ,14:21)], by = c("Gene" = "contig_geneID"))
# Visualize differences in codon bias
p_SCUO.1 <- ggplot(data = SCUO_merged, aes (x = 100*GC, y = SCUO, fill = Genome_ID))+
geom_point(size = 4, shape = 21, alpha = 0.7)+
scale_fill_manual(values = c(col_bac1, col_bac2, col_limno))+
theme_bw()+
facet_wrap(~GCx, ncol = 2)+
theme(axis.text=element_text(size=14), axis.title=element_text(size=20),
title=element_text(size=20), legend.text=element_text(size=14),
legend.background = element_rect(fill="transparent"),
axis.text.x = element_text(angle = 0, hjust = 1),
strip.text.x=element_text(size=18))+
ylab("SCUO")+
xlab("%GC")+
ylim(0,1)
print(p_SCUO.1)
# Subset to genes for which ko annotation is available
SCUO_merged_sb <- SCUO_merged[!is.na(SCUO_merged$level_A), ]
SCUO_merged_sb <- SCUO_merged_sb[SCUO_merged_sb$GCx == "GC_mean", ]
# Look at pathways enriched in high %GC
# SCUO_merged_sb[]
SCUO_merged_gen_gcmean <- SCUO_merged_gen %>% dplyr::filter(GCx == "GC_mean")
p_SCUO.2 <- ggplot(data = SCUO_merged_gen_gcmean, aes (x = Genome_ID, y = SCUO))+
geom_jitter(size = 3, alpha = 0.3, shape = 21, aes(fill = Genome_ID))+
geom_boxplot(alpha=0, size =1.5, color = "darkorange")+
# scale_fill_brewer(palette = "Accent")+
scale_fill_manual(values = c(col_bac1, col_bac2, col_limno))+
theme_bw()+
# facet_wrap(Genome_ID~GCx)+
theme(axis.text=element_text(size=14), axis.title=element_text(size=20),
title=element_text(size=20), legend.text=element_text(size=14),
legend.background = element_rect(fill="transparent"),
axis.text.x = element_text(angle = 45, hjust = 1),
strip.text.x=element_text(size=18))+
ylab("Codon bias - SCUO")+
xlab("")+
ylim(0,1)+
guides(fill=FALSE)+
scale_x_discrete(labels=c("Bacteroidetes MAG1" = paste("Bacteroidetes MAG1 (n=",table(SCUO_merged_gen_gcmean$Genome_ID)[1],")", sep = ""),
"Bacteroidetes MAG2" = paste("Bacteroidetes MAG2 (n=",table(SCUO_merged_gen_gcmean$Genome_ID)[2],")", sep = ""),
"Limnohabitans MAG" = paste("Limnohabitans MAG (n=",table(SCUO_merged_gen_gcmean$Genome_ID)[3],")", sep = ""))
)
#
print(p_SCUO.2)
tmp <- SCUO_merged_sb$genome_id
tmp2 <- cbind(SCUO_merged_sb$ko_id,
c(rep(col_limno, table(tmp)[3]), rep(col_bac1, table(tmp)[1]), rep(col_bac2, table(tmp)[2]))
)
write.table(tmp2, file = "All_KO.tsv", quote = FALSE,
col.names = FALSE, row.names = FALSE)9. PosiGene analysis for identifying genes under positive selection in the Limnohabitans MAG
Phylogenetic tree used in PosiGene analysis. Red branch indicates the genome for which PSG were tested. 
# Import results_short file
data_posi <- read.table("./posigene_analysis/result_tables/Limnohabitans_MAG_results.tsv", header = TRUE, fill = TRUE, sep = "\t")[, c("Transcript", "P.Value","FDR", "HA.foreground.omega")]
colnames(data_posi)[1] <- "Gene"
data_posi$Gene <- as.character(data_posi$Gene)
# Import mapping file to link gene IDs from PosiGene to
# those used by the IMG annotation (both are in headers from .genes.fna)
map_posi <- read.table("./Mapping_files/posiG_mapping.tsv")
colnames(map_posi) <- c("posi_geneID", "IMG_geneID")
map_posi$posi_geneID <- as.character(map_posi$posi_geneID)
# Filter out the genes that are under positive selection
# Taking threshold of adjusted p.value of 0.05 and FDR < 0.05
# Also remove dN/dS ratios of less than 15.
data_posi <- data_posi %>% filter(P.Value < 0.05 &
FDR < 0.05 & HA.foreground.omega < 15)
# Report summary
cat(paste("There are ", nrow(data_posi), " genes under positive selection in this MAG (P<0.05). This is ",round(100*nrow(data_posi)/nrow(map_posi),1), "% of all genes", sep = "")
)## There are 215 genes under positive selection in this MAG (P<0.05). This is 5.6% of all genes# Merge this data with the functional annotation (e.g. KO) of these genes
data_posi <- left_join(data_posi, map_posi, by = c("Gene" = "posi_geneID"))
data_posi_KO <- left_join(data_posi, merged_gc_ko, by = c("IMG_geneID" = "contig_geneID"))
# Optional: write tabel for quick view in iPath v2
# write.table(file = "KO_posiG.tsv", unique(data_posi_KO$ko_id), quote = FALSE,
# row.names = FALSE, col.names = FALSE)
# cat("Genes under positive selection with KO annotation")
# data_posi_KO[, c(1:6, 9:12, 20:29)]
# Focus on C-metabolism
data_posi_KO[data_posi_KO$ko_id %in% data_posi_KO$ko_id[data_posi_KO$level_B == "Carbohydrate metabolism"], ] %>%
ggplot(aes(x = ko_id, fill = level_C))+
geom_bar(stat="count", width=0.7, color = "black", size = 0.75)+
theme_bw()+
# facet_wrap(~level_B, ncol = 2)+
scale_fill_brewer(palette = "Paired")+
labs(x = "", y = "Count")+
theme(legend.position="bottom", axis.text.x = element_text(angle = 60, hjust = 1),
legend.text = element_text(size = 5))+
guides(fill=guide_legend(nrow=6,byrow=TRUE))
# Focus on energy metabolism
data_posi_KO %>% dplyr::filter(level_B == "Energy metabolism") %>%
ggplot(aes(x = ko_id, fill = level_C))+
geom_bar(stat="count", width=0.7, color = "black", size = 0.75)+
theme_bw()+
# facet_wrap(~level_B, ncol = 2)+
scale_fill_brewer(palette = "Paired")+
labs(x = "", y = "Count")+
theme(legend.position="bottom", axis.text.x = element_text(angle = 60, hjust = 1),
legend.text = element_text(size = 5))+
guides(fill=guide_legend(nrow=6,byrow=TRUE))
# Focus on translation
data_posi_KO %>% dplyr::filter(level_B == "Translation") %>%
ggplot(aes(x = ko_id, fill = level_C))+
geom_bar(stat="count", width=0.7, color = "black", size = 0.75)+
theme_bw()+
# facet_wrap(~level_B, ncol = 2)+
scale_fill_brewer(palette = "Paired")+
labs(x = "", y = "Count")+
theme(legend.position="bottom", axis.text.x = element_text(angle = 60, hjust = 1),
legend.text = element_text(size = 5))+
guides(fill=guide_legend(nrow=6,byrow=TRUE))
# Focus on secondary metabolite production
data_posi_KO %>% dplyr::filter(level_B == "Metabolism of cofactors and vitamins") %>%
ggplot(aes(x = ko_id, fill = level_C))+
geom_bar(stat="count", width=0.7, color = "black", size = 0.75)+
theme_bw()+
# facet_wrap(~level_B, ncol = 2)+
scale_fill_brewer(palette = "Paired")+
labs(x = "", y = "Count")+
theme(legend.position="bottom", axis.text.x = element_text(angle = 60, hjust = 1),
legend.text = element_text(size = 5))+
guides(fill=guide_legend(nrow=6,byrow=TRUE))
# Count number of unique genes involved in biosynthesis# Correlate genes under positive selection with their GC content
data_SCUO_posi <- SCUO_merged_gen_gcmean %>% filter(Genome_ID == "Limnohabitans MAG")
data_SCUO_posi <- droplevels(data_SCUO_posi)
data_SCUO_posi$posi_select <- data_SCUO_posi$Gene %in% data_posi$IMG_geneID
data_SCUO_posi$posi_select <- factor(data_SCUO_posi$posi_select, levels = c("FALSE", "TRUE"))
# merge with dN/dS ratio data
data_SCUO_posi <- left_join(data_SCUO_posi,
data_posi, by = c("Gene" = "IMG_geneID"))
data_SCUO_posi_only <- data_SCUO_posi %>% filter(posi_select == "TRUE")
# Selected annotation level (KO)
selected_KOlevels <- c("Signal transduction",
"Amino acid metabolism",
"Metabolism of other amino acids",
"Metabolism of cofactors and vitamins",
"Carbohydrate metabolism",
"Energy metabolism",
"Translation",
"Membrane transport",
"Biosynthesis of other secondary metabolites",
"Lipid metabolism",
"Membrane transport",
"Unknown"
)
# Evaluate correlation between codon bias and selection pressure
p_SCUO_posi <- ggplot(data = data_SCUO_posi, aes (x = GC, y = SCUO))+
geom_point(shape = 21, size = 3, fill = adjustcolor(col_limno, 0.1))+
geom_point(shape = 21, size = 4, fill = "red",
data = subset(data_SCUO_posi, posi_select == TRUE))+
# geom_boxplot(alpha=0, size =1.5, color = "darkorange")+
# scale_fill_brewer(palette = "Accent")+
# scale_fill_manual(values = c(adjustcolor(col_limno, 0.1), "darkorange"))+
# scale_size_manual(values = c(2.5,4))+
theme_bw()+
# facet_wrap(Genome_ID~GCx)+
theme(axis.text=element_text(size=14), axis.title=element_text(size=20),
title=element_text(size=20), legend.text=element_text(size=14),
legend.background = element_rect(fill="transparent"),
axis.text.x = element_text(angle = 45, hjust = 1),
strip.text.x=element_text(size=18))+
ylab("Codon bias - SCUO")+
xlab("%GC")+
ylim(0,1)+
guides(fill=FALSE, size = FALSE)
p_SCUO_posi
p_SCUO_posi2 <- ggplot(data = data_SCUO_posi, aes (x = posi_select, y = SCUO,
fill = posi_select))+
# geom_jitter(shape = 21, aes(fill = posi_select, size = posi_select), width = 0.2)+
geom_jitter(shape = 21, size = 3, width = 0.2, alpha = 0.5)+
geom_boxplot(alpha=0.5, size =1.5, color = "darkgrey", width = 0.35,
outlier.shape = NA)+
# scale_fill_brewer(palette = "Accent")+
scale_fill_manual(values = c(adjustcolor(col_limno, 0.1), adjustcolor("red", 0.5)))+
# scale_size_manual(values = c(2.5,4))+
theme_bw()+
# facet_wrap(Genome_ID~GCx)+
theme(axis.text=element_text(size=14), axis.title=element_text(size=20),
title=element_text(size=20), legend.text=element_text(size=14),
legend.background = element_rect(fill="transparent"),
axis.text.x = element_text(angle = 45, hjust = 1),
strip.text.x=element_text(size=18))+
ylab("Codon bias - SCUO")+
xlab("")+
ylim(0,1)+
guides(size = FALSE)
p_SCUO_posi2
p_SCUO_posi3 <- ggplot(data = data_SCUO_posi, aes (x = posi_select, y = GC,
fill = posi_select))+
# geom_jitter(shape = 21, aes(fill = posi_select, size = posi_select), width = 0.2)+
geom_jitter(shape = 21, size = 3, width = 0.2, alpha = 0.5)+
geom_boxplot(alpha=0.5, size =1.5, color = "darkgrey", width = 0.35,
outlier.shape = NA)+
# scale_fill_brewer(palette = "Accent")+
scale_fill_manual(values = c(adjustcolor(col_limno, 0.1), adjustcolor("red", 0.5)))+
# scale_size_manual(values = c(2.5,4))+
theme_bw()+
# facet_wrap(Genome_ID~GCx)+
theme(axis.text=element_text(size=14), axis.title=element_text(size=20),
title=element_text(size=20), legend.text=element_text(size=14),
legend.background = element_rect(fill="transparent"),
axis.text.x = element_text(angle = 45, hjust = 1),
strip.text.x=element_text(size=18))+
ylab("%GC")+
xlab("")+
ylim(0.5,0.9)+
guides(size = FALSE)
p_SCUO_posi3
p_SCUO_posi4 <- ggplot(data = data_SCUO_posi_only, aes (x = SCUO, y = sqrt(HA.foreground.omega)))+
geom_point(shape = 21, size = 3, fill = adjustcolor(col_limno, 0.1))+
# geom_point(shape = 21, size = 4, fill = "red",
# data = subset(data_SCUO_posi, posi_select == TRUE))+
# geom_boxplot(alpha=0, size =1.5, color = "darkorange")+
# scale_fill_brewer(palette = "Accent")+
# scale_fill_manual(values = c(adjustcolor(col_limno, 0.1), "darkorange"))+
# scale_size_manual(values = c(2.5,4))+
theme_bw()+
# facet_wrap(Genome_ID~GCx)+
theme(axis.text=element_text(size=14), axis.title=element_text(size=20),
title=element_text(size=20), legend.text=element_text(size=14),
legend.background = element_rect(fill="transparent"),
axis.text.x = element_text(angle = 45, hjust = 1),
strip.text.x=element_text(size=18))+
xlab("Codon bias - SCUO")+
ylab(expression(sqrt(dN/dS)))+
# ylim(0,1)+
geom_hline(yintercept = sqrt(1), color = "darkgreen", size = 2, linetype = 2)+
guides(fill=FALSE, size = FALSE)
p_SCUO_posi4
p_SCUO_posi5 <- ggplot(data = data_SCUO_posi_only, aes (x = 100*GC, y = sqrt(HA.foreground.omega)))+
geom_point(shape = 21, size = 3, fill = adjustcolor(col_limno, 0.1))+
# geom_point(shape = 21, size = 4, fill = "red",
# data = subset(data_SCUO_posi, posi_select == TRUE))+
# geom_boxplot(alpha=0, size =1.5, color = "darkorange")+
# scale_fill_brewer(palette = "Accent")+
# scale_fill_manual(values = c(adjustcolor(col_limno, 0.1), "darkorange"))+
# scale_size_manual(values = c(2.5,4))+
theme_bw()+
# facet_wrap(Genome_ID~GCx)+
theme(axis.text=element_text(size=14), axis.title=element_text(size=20),
title=element_text(size=20), legend.text=element_text(size=14),
legend.background = element_rect(fill="transparent"),
axis.text.x = element_text(angle = 45, hjust = 1),
strip.text.x=element_text(size=18))+
xlab("%GC")+
ylab(expression(sqrt(dN/dS)))+
# ylim(0,1)+
geom_hline(yintercept = sqrt(1), color = "darkgreen", size = 2, linetype = 2)+
guides(fill=FALSE, size = FALSE)
p_SCUO_posi5
p_SCUO_posi6 <- ggplot(data = data_posi_KO, aes (x = level_A, y = HA.foreground.omega,
fill = level_A))+
# geom_jitter(shape = 21, aes(fill = posi_select, size = posi_select), width = 0.2)+
geom_jitter(shape = 21, size = 3, width = 0.2, alpha = 0.5)+
geom_boxplot(alpha=0.5, size =1, color = "black", width = 0.35,
outlier.shape = NA)+
scale_fill_brewer(palette = "Accent")+
# scale_fill_manual(values = c(adjustcolor(col_limno, 0.1), adjustcolor("red", 0.5)))+
# scale_size_manual(values = c(2.5,4))+
theme_bw()+
# facet_wrap(Genome_ID~GCx)+
theme(axis.text=element_text(size=14), axis.title=element_text(size=20),
title=element_text(size=20), legend.text=element_text(size=14),
legend.background = element_rect(fill="transparent"),
axis.text.x = element_text(angle = 60, hjust = 1),
strip.text.x=element_text(size=18),
legend.position = "bottom")+
xlab("")+
ylab(expression(dN/dS))+
# ylim(0.5,0.9)+
guides(size = FALSE, fill = FALSE)
p_SCUO_posi6
# Extract level_C labels of genes in top 5 dN/ds ratio
# First filter out the genes with multiple annotations by
# selecting the annotation with the highest identity.
# data_posi_KO_unique <- data_posi_KO %>% group_by(Gene) %>%
# filter(percent_identity == max(percent_identity))
top <- 10
thresh <- sort(data_posi_KO[data_posi_KO$level_B %in% selected_KOlevels, ]$HA.foreground.omega, decreasing = TRUE)[top+1]
selected_KOlevels_labels <- data_posi_KO[data_posi_KO$level_B %in% selected_KOlevels, ]$level_C
selected_KOlevels_labels[data_posi_KO[data_posi_KO$level_B %in% selected_KOlevels, ]$HA.foreground.omega < thresh] <- ""
# Remove everything between brackets ([*])
selected_KOlevels_labels <- gsub(pattern = " *\\[.*?\\] *",
replacement = "", selected_KOlevels_labels)
# Remove everything before first space
selected_KOlevels_labels <- sub(".*? (.+)", "\\1", selected_KOlevels_labels)
p_SCUO_posi7 <- data_posi_KO[data_posi_KO$level_B %in% selected_KOlevels, ] %>%
ggplot(aes(x = level_B, y = HA.foreground.omega,
fill = level_B))+
# geom_jitter(shape = 21, aes(fill = posi_select, size = posi_select), width = 0.2)+
geom_jitter(shape = 21, size = 3, width = 0.2, alpha = 0.5)+
geom_boxplot(alpha=0.5, size =1, color = "black", width = 0.35,
outlier.shape = NA)+
scale_fill_brewer(palette = "Set3")+
# scale_fill_manual(values = c(adjustcolor(col_limno, 0.1), adjustcolor("red", 0.5)))+
# scale_size_manual(values = c(2.5,4))+
theme_bw()+
# facet_wrap(Genome_ID~GCx)+
theme(axis.text=element_text(size=12.5), axis.title=element_text(size=20),
title=element_text(size=20), legend.text=element_text(size=14),
legend.background = element_rect(fill="transparent"),
axis.text.x = element_text(angle = 60, hjust = 1),
strip.text.x=element_text(size=18),
legend.position = "bottom")+
xlab("")+
ylab(expression(dN/dS))+
# ylim(0.5,0.9)+
guides(size = FALSE, fill = FALSE)+
geom_label_repel(aes(label = selected_KOlevels_labels), fontface = 'bold', color = 'black',
box.padding = 0.35, point.padding = 0.5,
segment.color = "#3F4656",
size = 3,
# Width of the line segments.
segment.size = 1,
# Draw an arrow from the label to the data point.
arrow = arrow(length = unit(0.02, 'npc'), type = "closed"),
nudge_x = -0.1,
nudge_y = 0.6,
force = 10
)+
ylim(0,25)
p_SCUO_posi7
# Extract level_C labels of genes in top 5 dN/ds ratio
top <- 10
thresh <- sort(data_posi_KO[data_posi_KO$level_B %in% selected_KOlevels, ]$HA.foreground.omega, decreasing = TRUE)[top+1]
selected_KOlevels_labels <- data_posi_KO[data_posi_KO$level_B %in% selected_KOlevels, ]$ko_name
selected_KOlevels_labels[data_posi_KO[data_posi_KO$level_B %in% selected_KOlevels, ]$HA.foreground.omega < thresh] <- ""
# Remove everything between brackets ([*])
selected_KOlevels_labels <- gsub(pattern = " *\\[.*?\\] *",
replacement = "", selected_KOlevels_labels)
p_SCUO_posi8 <- data_posi_KO[data_posi_KO$level_B %in% selected_KOlevels, ] %>%
ggplot(aes(x = level_B, y = HA.foreground.omega,
fill = level_B))+
# geom_jitter(shape = 21, aes(fill = posi_select, size = posi_select), width = 0.2)+
geom_jitter(shape = 21, size = 3, width = 0.2, alpha = 0.5)+
geom_boxplot(alpha=0.5, size =1, color = "black", width = 0.35,
outlier.shape = NA)+
scale_fill_brewer(palette = "Set3")+
# scale_fill_manual(values = c(adjustcolor(col_limno, 0.1), adjustcolor("red", 0.5)))+
# scale_size_manual(values = c(2.5,4))+
theme_bw()+
# facet_wrap(Genome_ID~GCx)+
theme(axis.text=element_text(size=12.5), axis.title=element_text(size=20),
title=element_text(size=20), legend.text=element_text(size=14),
legend.background = element_rect(fill="transparent"),
axis.text.x = element_text(angle = 60, hjust = 1),
strip.text.x=element_text(size=18),
legend.position = "bottom")+
xlab("")+
ylab(expression(dN/dS))+
# ylim(0.5,0.9)+
guides(size = FALSE, fill = FALSE)+
geom_label_repel(aes(label = selected_KOlevels_labels), fontface = 'bold', color = 'black',
box.padding = 0.35, point.padding = 0.5,
segment.color = "#3F4656",
size = 3,
# Width of the line segments.
segment.size = 1,
# Draw an arrow from the label to the data point.
arrow = arrow(length = unit(0.02, 'npc'), type = "closed"),
nudge_x = -0.1,
nudge_y = 0.6,
force = 10
)+
ylim(0,25)
p_SCUO_posi8
10. Plasmid reconstruction using Recycler
files <- list.files("./recycler_plasmid/", pattern="*_length_cov")
files <- paste0("./recycler_plasmid/", files)
df_plasm_16 <- read.table(files[1], header = FALSE)
df_plasm_17 <- read.table(files[2], header = FALSE)
df_plasm_64 <- read.table(files[3], header = FALSE)
df_plasm_65 <- read.table(files[4], header = FALSE)
df_plasm <- rbind(df_plasm_16, df_plasm_17, df_plasm_64, df_plasm_65)
df_plasm <- as.character(df_plasm$V1); tmp <- df_plasm
df_plasm <- do.call(rbind, strsplit(df_plasm, "_"))
df_plasm <- df_plasm[, -c(1,3,5,7,8)]
colnames(df_plasm) <- c("NODE_ID", "Length", "avg_coverage")
df_plasm <- apply(df_plasm, 2, function(x) as.integer(x))
df_plasm <- data.frame(df_plasm, node_IDs = tmp)
# Filter out plasmid scaffolds lower than 1000 bp
p_plasm1 <- df_plasm %>% filter(Length > 1000) %>%
ggplot(aes(x = Length/1000, y = sqrt(avg_coverage), fill = Length))+
geom_point(shape = 21, size = 4)+ theme_bw()+
# scale_x_log10()+
# scale_y_log10()+
scale_fill_continuous()+
ylab(expression(sqrt(Average_coverage)))+
xlab("Length (kbp)")+
ggtitle("Coverage-Length distribution of reconstructed plasmid fragments")
p_plasm1